The primary objectives of the series are to provide useful reference books for researchers and scientists in academia, industry, and government, and also to offer textbooks for undergraduate and graduate courses in the areas of biostatistics and bioinformatics. The book series will provide comprehensive and unified presentations of statistical designs and analyses of important applications in biostatistics and bioinformatics, such as those in biological and biomedical research.
The scope of the series is wide, including applications of statistical methodology in biology, epidemiology, genetics, pharmaceutical science and clinical trials, public health, and medicine. The series is committed to providing easy to understand, state-of-the-art references and textbooks. In each volume, statistical concepts and methodologies will be illustrated through real world examples whenever possible.
Please contact us if you have an idea for a book for the series.
Edited
By Oleksandr Sverdlov, Joris van Dam
November 24, 2022
One of the hallmarks of the 21st century medicine is the emergence of digital therapeutics (DTx)—evidence-based, clinically validated digital technologies to prevent, diagnose, treat, and manage various diseases and medical conditions. DTx solutions have been gaining interest from patients, ...
By Ying Yuan, Ruitao Lin, J. Jack Lee
October 04, 2022
Bayesian adaptive designs provide a critical approach to improve the efficiency and success rate of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they forms the basis for the development and success of ...
By Catherine Legrand
September 26, 2022
Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular ...
By Ding-Geng (Din) Chen, Karl E. Peace
September 26, 2022
Review of the First Edition: The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; ...
By Yingwei Peng, Binbing Yu
September 26, 2022
Cure Models: Methods, Applications and Implementation is the first book in the last 25 years that provides a comprehensive and systematic introduction to the basics of modern cure models, including estimation, inference, and software. This book is useful for statistical researchers and graduate ...
By Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle
September 26, 2022
Structural equation modeling (SEM) is a very general and flexible multivariate technique that allows relationships among variables to be examined. The roots of SEM are in the social sciences. In writing this textbook, the authors look to make SEM accessible to a wider audience of researchers across...
Edited
By Harry Yang
September 19, 2022
The confluence of big data, AI, and machine learning has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital ...
By Lyle D. Broemeling
August 29, 2022
Bayesian Analysis of Infectious Diseases -COVID-19 and Beyond shows how the Bayesian approach can be used to analyze the evolutionary behavior of infectious diseases, including the coronavirus pandemic. The book describes the foundation of Bayesian statistics while explicating the biology and ...
By Thomas A. Gerds, Michael W. Kattan
August 29, 2022
Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient’s individualized probability of a ...
Edited
By Harry Yang, Binbing Yu
August 29, 2022
Real-world evidence (RWE) has been at the forefront of pharmaceutical innovations. It plays an important role in transforming drug development from a process aimed at meeting regulatory expectations to an operating model that leverages data from disparate sources to aid business, regulatory, and ...
By Demissie Alemayehu, Birol Emir, Michael Gaffney
August 01, 2022
With the critical role of statistics in the design, conduct, analysis and reporting of clinical trials or observational studies intended for regulatory purposes, numerous guidelines have been issued by regulatory authorities around the world focusing on statistical issues related to drug ...
Edited
By Kelly H. Zou, Lobna A. Salem, Amrit Ray
July 21, 2022
Real-world evidence is defined as evidence generated from real-world data outside randomized controlled trials. As scientific discoveries and methodologies continue to advance, real-world data and their companion technologies offer powerful new tools for evidence generation. Real-World Evidence in ...
